stable/freqtrade/exchange/exchange_helpers.py

"""
Functions to analyze ticker data with indicators and produce buy and sell signals
"""
import logging
import pandas as pd
from pandas import DataFrame, to_datetime

logger = logging.getLogger(__name__)


def parse_ticker_dataframe(ticker: list) -> DataFrame:
    """
    Analyses the trend for the given ticker history
    :param ticker: ticker list, as returned by exchange.async_get_candle_history
    :return: DataFrame
    """
    cols = ['date', 'open', 'high', 'low', 'close', 'volume']
    frame = DataFrame(ticker, columns=cols)

    frame['date'] = to_datetime(frame['date'],
                                unit='ms',
                                utc=True,
                                infer_datetime_format=True)

    # group by index and aggregate results to eliminate duplicate ticks
    frame = frame.groupby(by='date', as_index=False, sort=True).agg({
        'open': 'first',
        'high': 'max',
        'low': 'min',
        'close': 'last',
        'volume': 'max',
    })
    frame.drop(frame.tail(1).index, inplace=True)     # eliminate partial candle
    return frame


def order_book_to_dataframe(bids: list, asks: list) -> DataFrame:
    """
    Gets order book list, returns dataframe with below format per suggested by creslin
    -------------------------------------------------------------------
     b_sum       b_size       bids       asks       a_size       a_sum
    -------------------------------------------------------------------
    """
    cols = ['bids', 'b_size']

    bids_frame = DataFrame(bids, columns=cols)
    # add cumulative sum column
    bids_frame['b_sum'] = bids_frame['b_size'].cumsum()
    cols2 = ['asks', 'a_size']
    asks_frame = DataFrame(asks, columns=cols2)
    # add cumulative sum column
    asks_frame['a_sum'] = asks_frame['a_size'].cumsum()

    frame = pd.concat([bids_frame['b_sum'], bids_frame['b_size'], bids_frame['bids'],
                       asks_frame['asks'], asks_frame['a_size'], asks_frame['a_sum']], axis=1,
                      keys=['b_sum', 'b_size', 'bids', 'asks', 'a_size', 'a_sum'])
    # logger.info('order book %s', frame )
    return frame